UAV-based remote sensing and mechanistic modeling for rice lodging prediction
A predictive diagnostic tool that combines UAV remote sensing, proximal field camera tracking, and a mechanistic bending model to quantify and forecast lodging risk in rice and other grain crops. Enables early detection of lodging susceptibility by linking crop morphological changes and biomechanical resistance to environmental factors such as wind, rain, and temperature.
Overview
Crop lodging, the permanent displacement of stems from their upright position, is one of the most significant yet difficult-to-quantify causes of yield loss in grain crops. Because lodging is typically triggered by external abiotic forces such as wind, rain, or hail, traditional visual scoring methods are unreliable and subjective. This solution offers a predictive diagnostic platform that integrates UAV-based remote sensing, a fixed field camera tracking system (FCTS), and a mechanistic biomechanical model to assess lodging resistance in rice cultivars. The technology is designed to help breeders, agronomists, and growers identify lodging-prone crops before failure occurs, supporting more informed variety selection and crop management decisions.
Technical specifications
- Mechanistic bending model derived from laboratory force and displacement measurements on rice stems, capturing the force-strain relationship and stem rigidity across cultivars
- UAV-based proximal sensing using RGB and multispectral imagery to detect canopy-level variations associated with lodging susceptibility at different growth stages
- Fixed field camera tracking system (FCTS) for continuous monitoring of morphological changes in crop stems prior to lodging events
- Data fusion framework that combines UAV imagery, FCTS time-series observations, and environmental variables (wind speed, temperature, precipitation) into a unified lodging risk prediction
- Phenotyping pipeline capable of distinguishing lodging-resistant from lodging-susceptible rice cultivars based on image features and biomechanical parameters
- Preliminary validation has shown that lodging-resisting rice plants exhibit measurably higher rigidity under applied force, and that lodging versus non-lodging crops can be classified from UAV-acquired RGB and multispectral data using the theoretical model
Technology readiness level
The technology is currently at an early-to-mid stage of development. Laboratory characterization of force and displacement on different rice breeds has been initiated, and preliminary results confirm that the approach can differentiate lodging-resistant from lodging-susceptible cultivars using both biomechanical testing and UAV imagery. Planned next steps include controlled experiments across varying developmental stages, moisture, and nutrient conditions; refinement of the bending equation for the force-strain curve; and field-scale validation combining FCTS and UAV proximal sensing across multiple rice cultivars. The solution is ready for collaborative validation and pilot deployment in breeding programs or precision agriculture operations.
About Zhejiang University
Zhejiang University is a comprehensive public research university in Hangzhou, operating at large scale across multiple campuses with a strong mix of fundamental and applied research. Industry engages through research and technology parks, enterprise joint labs on campus, and prototyping facilities that de-risk scale-up. Integration with a major hospital system enables clinical translation, and proximity to the Yangtze River Delta links partners to manufacturing and digital-economy supply chains. Research is backed by competitive national funding, including the National Natural Science Foundation of China and the Ministry of Science and Technology, plus provincial and international sources. A dedicated technology transfer office supports IP, licensing, startup incubation, and access to its science park network.
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